---
title: "{{< iconify ph clipboard-text >}} Performance"
format: html
---
```{r}
#| include: false
library (dashboardr)
```
**THIS IS A MOCKUP VERSION PLEASE DO NOT CITE**
The **Performance** section measures participants' practical digital skills across a range of real-life tasks. Instead of self-reports, these items test what people *can actually do*: for example, searching for reliable information, recognizing AI-generated images, protecting their devices, or using AI tools effectively. The items cover ten areas of digital competence, from strategic and critical information skills to AI and generative AI skills, providing an overall picture of how well individuals can navigate today's digital environment.
```{r, echo=FALSE}
dashboardr::enable_modals()
```
```{r setup}
#| echo: false
#| warning: false
#| message: false
#| error: false
#| results: 'hide'
# Load required libraries
library(dashboardr)
library(dplyr)
library(highcharter)
# Global chunk options
knitr::opts_chunk$set(
echo = FALSE,
warning = FALSE,
message = FALSE,
error = FALSE,
fig.width = 12,
fig.height = 8,
dpi = 300
)
# Load data from dataset_4014obs.rds
data <- readRDS('dataset_4014obs.rds')
# Data summary
cat('Dataset loaded:', nrow(data), 'rows,', ncol(data), 'columns\n')
# Create filtered datasets
# Each filter is applied once and reused across visualizations
data_filtered_984a0efe <- data %>% dplyr::filter(wave == 1)
data_filtered_4af682fd <- data %>% dplyr::filter(wave == 2)
```
```{r, echo=FALSE, message=FALSE, warning=FALSE, results='asis'}
# Use dashboardr's loading overlay function
dashboardr::add_loading_overlay("Loading", 1, theme = "light")
```
```{r, echo=FALSE, results='asis'}
cat(as.character(dashboardr::modal_content(
modal_id = 'PDCCS1R',
text = '<h2>Digital Content Creation: Performance Questions</h2>
<img src="https://placehold.co/600x400/EEE/31343C" style="max-width:100%; height:auto;">
<p>Information literacy scores were highest at 85%.
Participants excelled at search strategies and
source evaluation.</p>'
)))
```
## {{< iconify ph wallet-fill >}} Transactional
**Transactional Skills** cover the ability to complete official tasks online: handling tax matters, making digital payments, arranging healthcare, using digital identification (DigID), and uploading documents for online services.
```{r, echo=FALSE, message=FALSE, warning=FALSE}
create_blockquote("Performance tasks: proportion correct or selected. Where items are multi-select, we show the share selecting each action.", preset = "question")
```
[ {{< iconify ph cards >}} See all Transactional results ](transactional.html)
::: {.panel-tabset}
### {{< iconify ph number-circle-one-fill >}} Wave 1
::: {.panel-tabset}
##### {{< iconify ph users-fill >}} Overall
```{r perf-trans-wave1-overall}
# Identify trust/safety icon (webshop)
result <- create_stackedbars(
data = data_filtered_984a0efe %>% tidyr::drop_na(PTS1R),
title = "Identify trust/safety icon (webshop)",
questions = "PTS1R",
question_labels = "Identify trust/safety icon (webshop)",
stacked_type = "percent",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
horizontal = TRUE,
x_label = "",
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Incorrect", "Correct"),
stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
stack_order = c("Incorrect", "Correct"),
stack_label = NULL,
weight_var = "weging_GAMO"
)
result
```
##### {{< iconify mdi:human-male-male-child >}} Age
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-trans-wave1-age-item1}
# Identify trust/safety icon (webshop)
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(AgeGroup, PTS1R),
title = "Identify trust/safety icon (webshop)",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Incorrect", "Correct"),
stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
stack_order = c("Incorrect", "Correct"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PTS1R"
)
result
```
:::
##### {{< iconify mdi gender-transgender >}} Gender
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-trans-wave1-gender-item1}
# Identify trust/safety icon (webshop)
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(geslacht, PTS1R),
title = "Identify trust/safety icon (webshop)",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Incorrect", "Correct"),
stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
stack_order = c("Incorrect", "Correct"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PTS1R"
)
result
```
:::
##### {{< iconify ph graduation-cap-fill >}} Education
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-trans-wave1-edu-item1}
# Identify trust/safety icon (webshop)
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(Education, PTS1R),
title = "Identify trust/safety icon (webshop)",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Incorrect", "Correct"),
stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
stack_order = c("Incorrect", "Correct"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PTS1R"
)
result
```
:::
:::
### {{< iconify ph number-circle-two-fill >}} Wave 2
::: {.panel-tabset}
##### {{< iconify ph users-fill >}} Overall
```{r perf-trans-wave2-overall}
# Identify trust/safety icon (webshop)
result <- create_stackedbars(
data = data_filtered_4af682fd %>% tidyr::drop_na(PTS1R),
title = "Identify trust/safety icon (webshop)",
questions = "PTS1R",
question_labels = "Identify trust/safety icon (webshop)",
stacked_type = "percent",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
horizontal = TRUE,
x_label = "",
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Incorrect", "Correct"),
stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
stack_order = c("Incorrect", "Correct"),
stack_label = NULL,
weight_var = "weging_GAMO"
)
result
```
##### {{< iconify mdi:human-male-male-child >}} Age
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-trans-wave2-age-item1}
# Identify trust/safety icon (webshop)
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(AgeGroup, PTS1R),
title = "Identify trust/safety icon (webshop)",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Incorrect", "Correct"),
stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
stack_order = c("Incorrect", "Correct"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PTS1R"
)
result
```
:::
##### {{< iconify mdi gender-transgender >}} Gender
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-trans-wave2-gender-item1}
# Identify trust/safety icon (webshop)
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(geslacht, PTS1R),
title = "Identify trust/safety icon (webshop)",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Incorrect", "Correct"),
stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
stack_order = c("Incorrect", "Correct"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PTS1R"
)
result
```
:::
##### {{< iconify ph graduation-cap-fill >}} Education
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-trans-wave2-edu-item1}
# Identify trust/safety icon (webshop)
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(Education, PTS1R),
title = "Identify trust/safety icon (webshop)",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Incorrect", "Correct"),
stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
stack_order = c("Incorrect", "Correct"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PTS1R"
)
result
```
:::
:::
### {{< iconify ph chart-line-fill >}} Over Time
::: {.panel-tabset}
##### {{< iconify ph users-fill >}} Overall
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-trans-overtime-overall-item1}
# Identify trust/safety icon (webshop)
result <- create_timeline(
data = data,
title = "Identify trust/safety icon (webshop)",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
response_filter_label = "Percentage who selected/answered correctly",
response_filter_combine = TRUE,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
response_var = "PTS1R"
)
result
```
:::
##### {{< iconify mdi:human-male-male-child >}} Age
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-trans-overtime-age-item1}
# Identify trust/safety icon (webshop)
result <- create_timeline(
data = data,
title = "Identify trust/safety icon (webshop)",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PTS1R",
group_var = "AgeGroup"
)
result
```
:::
##### {{< iconify mdi gender-transgender >}} Gender
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-trans-overtime-gender-item1}
# Identify trust/safety icon (webshop)
result <- create_timeline(
data = data,
title = "Identify trust/safety icon (webshop)",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PTS1R",
group_var = "geslacht"
)
result
```
:::
##### {{< iconify ph graduation-cap-fill >}} Education
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-trans-overtime-edu-item1}
# Identify trust/safety icon (webshop)
result <- create_timeline(
data = data,
title = "Identify trust/safety icon (webshop)",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PTS1R",
group_var = "Education"
)
result
```
:::
:::
:::
## {{< iconify ph robot-fill >}} AI
**AI Skills** assess understanding and recognition of artificial intelligence in everyday applications: recognizing when websites/apps use AI, identifying AI-recommended content, and understanding how AI personalizes digital experiences.
```{r, echo=FALSE, message=FALSE, warning=FALSE}
create_blockquote("Look closely at the pictures below. Which picture is not made with artificial intelligence (AI)?", preset = "question")
```
[ {{< iconify ph cards >}} See all AI results ](ai.html)
::: {.panel-tabset}
### {{< iconify ph number-circle-one-fill >}} Wave 1
::: {.panel-tabset}
##### {{< iconify ph users-fill >}} Overall
```{r perf-ai-wave1-overall}
#
result <- create_stackedbars(
data = data_filtered_984a0efe %>% tidyr::drop_na(PAIS2_1, PAIS2_2, PAIS2_3, PAIS2_4, PAIS2_5, PAIS2_6, PAIS2_7, PAIS2_8, PAIS2_9),
title = "",
questions = c("PAIS2_1", "PAIS2_2", "PAIS2_3", "PAIS2_4", "PAIS2_5", "PAIS2_6", "PAIS2_7", "PAIS2_8", "PAIS2_9"),
question_labels = c("Google", "Netflix", "Whatsapp", "Facebook", "Bol.com", "DigID", "NOS News", "Albert Heijn", "TikTok"),
stacked_type = "percent",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
horizontal = TRUE,
x_label = "",
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
stack_label = NULL,
weight_var = "weging_GAMO"
)
result
```
##### {{< iconify mdi:human-male-male-child >}} Age
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-ai-wave1-age-item1}
# Google
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(AgeGroup, PAIS2_1),
title = "Google",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PAIS2_1"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 2
```{r perf-ai-wave1-age-item2}
# Netflix
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(AgeGroup, PAIS2_2),
title = "Netflix",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PAIS2_2"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 3
```{r perf-ai-wave1-age-item3}
# Whatsapp
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(AgeGroup, PAIS2_3),
title = "Whatsapp",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PAIS2_3"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 4
```{r perf-ai-wave1-age-item4}
# Facebook
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(AgeGroup, PAIS2_4),
title = "Facebook",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PAIS2_4"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 5
```{r perf-ai-wave1-age-item5}
# Bol.com
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(AgeGroup, PAIS2_5),
title = "Bol.com",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PAIS2_5"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 6
```{r perf-ai-wave1-age-item6}
# DigID
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(AgeGroup, PAIS2_6),
title = "DigID",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PAIS2_6"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 7
```{r perf-ai-wave1-age-item7}
# NOS News
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(AgeGroup, PAIS2_7),
title = "NOS News",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PAIS2_7"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 8
```{r perf-ai-wave1-age-item8}
# Albert Heijn
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(AgeGroup, PAIS2_8),
title = "Albert Heijn",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PAIS2_8"
)
result
```
###### item9
```{r perf-ai-wave1-age-item9}
# TikTok
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(AgeGroup, PAIS2_9),
title = "TikTok",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PAIS2_9"
)
result
```
:::
##### {{< iconify mdi gender-transgender >}} Gender
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-ai-wave1-gender-item1}
# Google
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(geslacht, PAIS2_1),
title = "Google",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PAIS2_1"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 2
```{r perf-ai-wave1-gender-item2}
# Netflix
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(geslacht, PAIS2_2),
title = "Netflix",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PAIS2_2"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 3
```{r perf-ai-wave1-gender-item3}
# Whatsapp
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(geslacht, PAIS2_3),
title = "Whatsapp",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PAIS2_3"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 4
```{r perf-ai-wave1-gender-item4}
# Facebook
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(geslacht, PAIS2_4),
title = "Facebook",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PAIS2_4"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 5
```{r perf-ai-wave1-gender-item5}
# Bol.com
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(geslacht, PAIS2_5),
title = "Bol.com",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PAIS2_5"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 6
```{r perf-ai-wave1-gender-item6}
# DigID
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(geslacht, PAIS2_6),
title = "DigID",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PAIS2_6"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 7
```{r perf-ai-wave1-gender-item7}
# NOS News
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(geslacht, PAIS2_7),
title = "NOS News",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PAIS2_7"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 8
```{r perf-ai-wave1-gender-item8}
# Albert Heijn
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(geslacht, PAIS2_8),
title = "Albert Heijn",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PAIS2_8"
)
result
```
###### item9
```{r perf-ai-wave1-gender-item9}
# TikTok
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(geslacht, PAIS2_9),
title = "TikTok",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PAIS2_9"
)
result
```
:::
##### {{< iconify ph graduation-cap-fill >}} Education
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-ai-wave1-edu-item1}
# Google
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(Education, PAIS2_1),
title = "Google",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PAIS2_1"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 2
```{r perf-ai-wave1-edu-item2}
# Netflix
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(Education, PAIS2_2),
title = "Netflix",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PAIS2_2"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 3
```{r perf-ai-wave1-edu-item3}
# Whatsapp
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(Education, PAIS2_3),
title = "Whatsapp",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PAIS2_3"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 4
```{r perf-ai-wave1-edu-item4}
# Facebook
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(Education, PAIS2_4),
title = "Facebook",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PAIS2_4"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 5
```{r perf-ai-wave1-edu-item5}
# Bol.com
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(Education, PAIS2_5),
title = "Bol.com",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PAIS2_5"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 6
```{r perf-ai-wave1-edu-item6}
# DigID
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(Education, PAIS2_6),
title = "DigID",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PAIS2_6"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 7
```{r perf-ai-wave1-edu-item7}
# NOS News
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(Education, PAIS2_7),
title = "NOS News",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PAIS2_7"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 8
```{r perf-ai-wave1-edu-item8}
# Albert Heijn
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(Education, PAIS2_8),
title = "Albert Heijn",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PAIS2_8"
)
result
```
###### item9
```{r perf-ai-wave1-edu-item9}
# TikTok
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(Education, PAIS2_9),
title = "TikTok",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PAIS2_9"
)
result
```
:::
:::
### {{< iconify ph number-circle-two-fill >}} Wave 2
::: {.panel-tabset}
##### {{< iconify ph users-fill >}} Overall
```{r perf-ai-wave2-overall}
#
result <- create_stackedbars(
data = data_filtered_4af682fd %>% tidyr::drop_na(PAIS2_1, PAIS2_2, PAIS2_3, PAIS2_4, PAIS2_5, PAIS2_6, PAIS2_7, PAIS2_8, PAIS2_9),
title = "",
questions = c("PAIS2_1", "PAIS2_2", "PAIS2_3", "PAIS2_4", "PAIS2_5", "PAIS2_6", "PAIS2_7", "PAIS2_8", "PAIS2_9"),
question_labels = c("Google", "Netflix", "Whatsapp", "Facebook", "Bol.com", "DigID", "NOS News", "Albert Heijn", "TikTok"),
stacked_type = "percent",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
horizontal = TRUE,
x_label = "",
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
stack_label = NULL,
weight_var = "weging_GAMO"
)
result
```
##### {{< iconify mdi:human-male-male-child >}} Age
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-ai-wave2-age-item1}
# Google
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(AgeGroup, PAIS2_1),
title = "Google",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PAIS2_1"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 2
```{r perf-ai-wave2-age-item2}
# Netflix
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(AgeGroup, PAIS2_2),
title = "Netflix",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PAIS2_2"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 3
```{r perf-ai-wave2-age-item3}
# Whatsapp
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(AgeGroup, PAIS2_3),
title = "Whatsapp",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PAIS2_3"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 4
```{r perf-ai-wave2-age-item4}
# Facebook
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(AgeGroup, PAIS2_4),
title = "Facebook",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PAIS2_4"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 5
```{r perf-ai-wave2-age-item5}
# Bol.com
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(AgeGroup, PAIS2_5),
title = "Bol.com",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PAIS2_5"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 6
```{r perf-ai-wave2-age-item6}
# DigID
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(AgeGroup, PAIS2_6),
title = "DigID",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PAIS2_6"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 7
```{r perf-ai-wave2-age-item7}
# NOS News
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(AgeGroup, PAIS2_7),
title = "NOS News",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PAIS2_7"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 8
```{r perf-ai-wave2-age-item8}
# Albert Heijn
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(AgeGroup, PAIS2_8),
title = "Albert Heijn",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PAIS2_8"
)
result
```
###### item9
```{r perf-ai-wave2-age-item9}
# TikTok
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(AgeGroup, PAIS2_9),
title = "TikTok",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PAIS2_9"
)
result
```
:::
##### {{< iconify mdi gender-transgender >}} Gender
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-ai-wave2-gender-item1}
# Google
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(geslacht, PAIS2_1),
title = "Google",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PAIS2_1"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 2
```{r perf-ai-wave2-gender-item2}
# Netflix
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(geslacht, PAIS2_2),
title = "Netflix",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PAIS2_2"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 3
```{r perf-ai-wave2-gender-item3}
# Whatsapp
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(geslacht, PAIS2_3),
title = "Whatsapp",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PAIS2_3"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 4
```{r perf-ai-wave2-gender-item4}
# Facebook
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(geslacht, PAIS2_4),
title = "Facebook",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PAIS2_4"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 5
```{r perf-ai-wave2-gender-item5}
# Bol.com
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(geslacht, PAIS2_5),
title = "Bol.com",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PAIS2_5"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 6
```{r perf-ai-wave2-gender-item6}
# DigID
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(geslacht, PAIS2_6),
title = "DigID",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PAIS2_6"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 7
```{r perf-ai-wave2-gender-item7}
# NOS News
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(geslacht, PAIS2_7),
title = "NOS News",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PAIS2_7"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 8
```{r perf-ai-wave2-gender-item8}
# Albert Heijn
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(geslacht, PAIS2_8),
title = "Albert Heijn",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PAIS2_8"
)
result
```
###### item9
```{r perf-ai-wave2-gender-item9}
# TikTok
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(geslacht, PAIS2_9),
title = "TikTok",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PAIS2_9"
)
result
```
:::
##### {{< iconify ph graduation-cap-fill >}} Education
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-ai-wave2-edu-item1}
# Google
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(Education, PAIS2_1),
title = "Google",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PAIS2_1"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 2
```{r perf-ai-wave2-edu-item2}
# Netflix
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(Education, PAIS2_2),
title = "Netflix",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PAIS2_2"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 3
```{r perf-ai-wave2-edu-item3}
# Whatsapp
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(Education, PAIS2_3),
title = "Whatsapp",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PAIS2_3"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 4
```{r perf-ai-wave2-edu-item4}
# Facebook
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(Education, PAIS2_4),
title = "Facebook",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PAIS2_4"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 5
```{r perf-ai-wave2-edu-item5}
# Bol.com
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(Education, PAIS2_5),
title = "Bol.com",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PAIS2_5"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 6
```{r perf-ai-wave2-edu-item6}
# DigID
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(Education, PAIS2_6),
title = "DigID",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PAIS2_6"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 7
```{r perf-ai-wave2-edu-item7}
# NOS News
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(Education, PAIS2_7),
title = "NOS News",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PAIS2_7"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 8
```{r perf-ai-wave2-edu-item8}
# Albert Heijn
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(Education, PAIS2_8),
title = "Albert Heijn",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PAIS2_8"
)
result
```
###### item9
```{r perf-ai-wave2-edu-item9}
# TikTok
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(Education, PAIS2_9),
title = "TikTok",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Not selected", "Selected"),
stack_map_values = list("1" = "Selected", "0" = "Not selected"),
stack_order = c("Not selected", "Selected"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PAIS2_9"
)
result
```
:::
:::
### {{< iconify ph chart-line-fill >}} Over Time
::: {.panel-tabset}
##### {{< iconify ph users-fill >}} Overall
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-ai-overtime-overall-item1}
# Google
result <- create_timeline(
data = data,
title = "Google",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
response_filter_label = "Percentage who selected/answered correctly",
response_filter_combine = TRUE,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
response_var = "PAIS2_1"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 2
```{r perf-ai-overtime-overall-item2}
# Netflix
result <- create_timeline(
data = data,
title = "Netflix",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
response_filter_label = "Percentage who selected/answered correctly",
response_filter_combine = TRUE,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
response_var = "PAIS2_2"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 3
```{r perf-ai-overtime-overall-item3}
# Whatsapp
result <- create_timeline(
data = data,
title = "Whatsapp",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
response_filter_label = "Percentage who selected/answered correctly",
response_filter_combine = TRUE,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
response_var = "PAIS2_3"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 4
```{r perf-ai-overtime-overall-item4}
# Facebook
result <- create_timeline(
data = data,
title = "Facebook",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
response_filter_label = "Percentage who selected/answered correctly",
response_filter_combine = TRUE,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
response_var = "PAIS2_4"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 5
```{r perf-ai-overtime-overall-item5}
# Bol.com
result <- create_timeline(
data = data,
title = "Bol.com",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
response_filter_label = "Percentage who selected/answered correctly",
response_filter_combine = TRUE,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
response_var = "PAIS2_5"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 6
```{r perf-ai-overtime-overall-item6}
# DigID
result <- create_timeline(
data = data,
title = "DigID",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
response_filter_label = "Percentage who selected/answered correctly",
response_filter_combine = TRUE,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
response_var = "PAIS2_6"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 7
```{r perf-ai-overtime-overall-item7}
# NOS News
result <- create_timeline(
data = data,
title = "NOS News",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
response_filter_label = "Percentage who selected/answered correctly",
response_filter_combine = TRUE,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
response_var = "PAIS2_7"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 8
```{r perf-ai-overtime-overall-item8}
# Albert Heijn
result <- create_timeline(
data = data,
title = "Albert Heijn",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
response_filter_label = "Percentage who selected/answered correctly",
response_filter_combine = TRUE,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
response_var = "PAIS2_8"
)
result
```
###### item9
```{r perf-ai-overtime-overall-item9}
# TikTok
result <- create_timeline(
data = data,
title = "TikTok",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
response_filter_label = "Percentage who selected/answered correctly",
response_filter_combine = TRUE,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
response_var = "PAIS2_9"
)
result
```
:::
##### {{< iconify mdi:human-male-male-child >}} Age
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-ai-overtime-age-item1}
# Google
result <- create_timeline(
data = data,
title = "Google",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_1",
group_var = "AgeGroup"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 2
```{r perf-ai-overtime-age-item2}
# Netflix
result <- create_timeline(
data = data,
title = "Netflix",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_2",
group_var = "AgeGroup"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 3
```{r perf-ai-overtime-age-item3}
# Whatsapp
result <- create_timeline(
data = data,
title = "Whatsapp",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_3",
group_var = "AgeGroup"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 4
```{r perf-ai-overtime-age-item4}
# Facebook
result <- create_timeline(
data = data,
title = "Facebook",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_4",
group_var = "AgeGroup"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 5
```{r perf-ai-overtime-age-item5}
# Bol.com
result <- create_timeline(
data = data,
title = "Bol.com",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_5",
group_var = "AgeGroup"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 6
```{r perf-ai-overtime-age-item6}
# DigID
result <- create_timeline(
data = data,
title = "DigID",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_6",
group_var = "AgeGroup"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 7
```{r perf-ai-overtime-age-item7}
# NOS News
result <- create_timeline(
data = data,
title = "NOS News",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_7",
group_var = "AgeGroup"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 8
```{r perf-ai-overtime-age-item8}
# Albert Heijn
result <- create_timeline(
data = data,
title = "Albert Heijn",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_8",
group_var = "AgeGroup"
)
result
```
###### item9
```{r perf-ai-overtime-age-item9}
# TikTok
result <- create_timeline(
data = data,
title = "TikTok",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_9",
group_var = "AgeGroup"
)
result
```
:::
##### {{< iconify mdi gender-transgender >}} Gender
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-ai-overtime-gender-item1}
# Google
result <- create_timeline(
data = data,
title = "Google",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_1",
group_var = "geslacht"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 2
```{r perf-ai-overtime-gender-item2}
# Netflix
result <- create_timeline(
data = data,
title = "Netflix",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_2",
group_var = "geslacht"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 3
```{r perf-ai-overtime-gender-item3}
# Whatsapp
result <- create_timeline(
data = data,
title = "Whatsapp",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_3",
group_var = "geslacht"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 4
```{r perf-ai-overtime-gender-item4}
# Facebook
result <- create_timeline(
data = data,
title = "Facebook",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_4",
group_var = "geslacht"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 5
```{r perf-ai-overtime-gender-item5}
# Bol.com
result <- create_timeline(
data = data,
title = "Bol.com",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_5",
group_var = "geslacht"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 6
```{r perf-ai-overtime-gender-item6}
# DigID
result <- create_timeline(
data = data,
title = "DigID",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_6",
group_var = "geslacht"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 7
```{r perf-ai-overtime-gender-item7}
# NOS News
result <- create_timeline(
data = data,
title = "NOS News",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_7",
group_var = "geslacht"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 8
```{r perf-ai-overtime-gender-item8}
# Albert Heijn
result <- create_timeline(
data = data,
title = "Albert Heijn",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_8",
group_var = "geslacht"
)
result
```
###### item9
```{r perf-ai-overtime-gender-item9}
# TikTok
result <- create_timeline(
data = data,
title = "TikTok",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_9",
group_var = "geslacht"
)
result
```
:::
##### {{< iconify ph graduation-cap-fill >}} Education
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-ai-overtime-edu-item1}
# Google
result <- create_timeline(
data = data,
title = "Google",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_1",
group_var = "Education"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 2
```{r perf-ai-overtime-edu-item2}
# Netflix
result <- create_timeline(
data = data,
title = "Netflix",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_2",
group_var = "Education"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 3
```{r perf-ai-overtime-edu-item3}
# Whatsapp
result <- create_timeline(
data = data,
title = "Whatsapp",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_3",
group_var = "Education"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 4
```{r perf-ai-overtime-edu-item4}
# Facebook
result <- create_timeline(
data = data,
title = "Facebook",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_4",
group_var = "Education"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 5
```{r perf-ai-overtime-edu-item5}
# Bol.com
result <- create_timeline(
data = data,
title = "Bol.com",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_5",
group_var = "Education"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 6
```{r perf-ai-overtime-edu-item6}
# DigID
result <- create_timeline(
data = data,
title = "DigID",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_6",
group_var = "Education"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 7
```{r perf-ai-overtime-edu-item7}
# NOS News
result <- create_timeline(
data = data,
title = "NOS News",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_7",
group_var = "Education"
)
result
```
###### {{< iconify ph chat-circle-fill >}} Question 8
```{r perf-ai-overtime-edu-item8}
# Albert Heijn
result <- create_timeline(
data = data,
title = "Albert Heijn",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_8",
group_var = "Education"
)
result
```
###### item9
```{r perf-ai-overtime-edu-item9}
# TikTok
result <- create_timeline(
data = data,
title = "TikTok",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS2_9",
group_var = "Education"
)
result
```
:::
:::
:::
## {{< iconify ph magic-wand-fill >}} Gen AI
**Generative AI Skills** measure competencies with AI tools like ChatGPT: knowing how to verify AI-generated information, writing effective prompts, detecting AI-generated content, and understanding GenAI's capabilities and limitations.
```{r, echo=FALSE, message=FALSE, warning=FALSE}
create_blockquote("Performance tasks: proportion correct or selected. Where items are multi-select, we show the share selecting each action.", preset = "question")
```
[ {{< iconify ph cards >}} See all Gen AI results ](gen_ai.html)
::: {.panel-tabset}
### {{< iconify ph number-circle-one-fill >}} Wave 1
::: {.panel-tabset}
##### {{< iconify ph users-fill >}} Overall
```{r perf-genai-wave1-overall}
# Spot non-AI image
result <- create_stackedbars(
data = data_filtered_984a0efe %>% tidyr::drop_na(PAIS1R),
title = "Spot non-AI image",
questions = "PAIS1R",
question_labels = "Spot non-AI image",
stacked_type = "percent",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
horizontal = TRUE,
x_label = "",
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Incorrect", "Correct"),
stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
stack_order = c("Incorrect", "Correct"),
stack_label = NULL,
weight_var = "weging_GAMO"
)
result
```
##### {{< iconify mdi:human-male-male-child >}} Age
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-genai-wave1-age-item1}
# Spot non-AI image
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(AgeGroup, PAIS1R),
title = "Spot non-AI image",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Incorrect", "Correct"),
stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
stack_order = c("Incorrect", "Correct"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PAIS1R"
)
result
```
:::
##### {{< iconify mdi gender-transgender >}} Gender
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-genai-wave1-gender-item1}
# Spot non-AI image
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(geslacht, PAIS1R),
title = "Spot non-AI image",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Incorrect", "Correct"),
stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
stack_order = c("Incorrect", "Correct"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PAIS1R"
)
result
```
:::
##### {{< iconify ph graduation-cap-fill >}} Education
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-genai-wave1-edu-item1}
# Spot non-AI image
result <- create_stackedbar(
data = data_filtered_984a0efe %>% tidyr::drop_na(Education, PAIS1R),
title = "Spot non-AI image",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Incorrect", "Correct"),
stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
stack_order = c("Incorrect", "Correct"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PAIS1R"
)
result
```
:::
:::
### {{< iconify ph number-circle-two-fill >}} Wave 2
::: {.panel-tabset}
##### {{< iconify ph users-fill >}} Overall
```{r perf-genai-wave2-overall}
# Spot non-AI image
result <- create_stackedbars(
data = data_filtered_4af682fd %>% tidyr::drop_na(PAIS1R),
title = "Spot non-AI image",
questions = "PAIS1R",
question_labels = "Spot non-AI image",
stacked_type = "percent",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
horizontal = TRUE,
x_label = "",
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Incorrect", "Correct"),
stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
stack_order = c("Incorrect", "Correct"),
stack_label = NULL,
weight_var = "weging_GAMO"
)
result
```
##### {{< iconify mdi:human-male-male-child >}} Age
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-genai-wave2-age-item1}
# Spot non-AI image
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(AgeGroup, PAIS1R),
title = "Spot non-AI image",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Incorrect", "Correct"),
stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
stack_order = c("Incorrect", "Correct"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "AgeGroup",
stack_var = "PAIS1R"
)
result
```
:::
##### {{< iconify mdi gender-transgender >}} Gender
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-genai-wave2-gender-item1}
# Spot non-AI image
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(geslacht, PAIS1R),
title = "Spot non-AI image",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Incorrect", "Correct"),
stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
stack_order = c("Incorrect", "Correct"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "geslacht",
stack_var = "PAIS1R"
)
result
```
:::
##### {{< iconify ph graduation-cap-fill >}} Education
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-genai-wave2-edu-item1}
# Spot non-AI image
result <- create_stackedbar(
data = data_filtered_4af682fd %>% tidyr::drop_na(Education, PAIS1R),
title = "Spot non-AI image",
stacked_type = "percent",
horizontal = TRUE,
stack_breaks = c(0, 10, 20, 30),
stack_bin_labels = c("Incorrect", "Correct"),
stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
stack_order = c("Incorrect", "Correct"),
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
x_var = "Education",
stack_var = "PAIS1R"
)
result
```
:::
:::
### {{< iconify ph chart-line-fill >}} Over Time
::: {.panel-tabset}
##### {{< iconify ph users-fill >}} Overall
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-genai-overtime-overall-item1}
# Spot non-AI image
result <- create_timeline(
data = data,
title = "Spot non-AI image",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
response_filter_label = "Percentage who selected/answered correctly",
response_filter_combine = TRUE,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
weight_var = "weging_GAMO",
response_var = "PAIS1R"
)
result
```
:::
##### {{< iconify mdi:human-male-male-child >}} Age
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-genai-overtime-age-item1}
# Spot non-AI image
result <- create_timeline(
data = data,
title = "Spot non-AI image",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS1R",
group_var = "AgeGroup"
)
result
```
:::
##### {{< iconify mdi gender-transgender >}} Gender
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-genai-overtime-gender-item1}
# Spot non-AI image
result <- create_timeline(
data = data,
title = "Spot non-AI image",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS1R",
group_var = "geslacht"
)
result
```
:::
##### {{< iconify ph graduation-cap-fill >}} Education
::: {.panel-tabset}
###### {{< iconify ph chat-circle-fill >}} Question 1
```{r perf-genai-overtime-edu-item1}
# Spot non-AI image
result <- create_timeline(
data = data,
title = "Spot non-AI image",
time_var = "wave_time_label",
chart_type = "line",
response_filter = 1,
y_min = 0,
y_max = 100,
x_label = "",
y_label = "Percentage who selected/answered correctly",
color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
response_filter_label = NULL,
weight_var = "weging_GAMO",
response_var = "PAIS1R",
group_var = "Education"
)
result
```
:::
:::
:::
```{=html}
<nav class='pagination-nav' role='navigation' aria-label='Page navigation'>
<div class='pagination-container'>
<a href='performance_p2.html' class='pagination-btn pagination-prev' aria-label='Previous page'>
<svg class='pagination-icon' width='18' height='18' viewBox='0 0 20 20' fill='none' xmlns='http://www.w3.org/2000/svg'>
<path d='M12 16L6 10L12 4' stroke='currentColor' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'/>
</svg>
</a>
<div class='pagination-info'>
<input type='number' class='pagination-input' id='page-input' min='1' max='3' value='3' aria-label='Current page'>
<span class='pagination-separator'>/ 3</span>
</div>
<button class='pagination-btn pagination-next pagination-disabled' disabled aria-label='Next page'>
<svg class='pagination-icon' width='18' height='18' viewBox='0 0 20 20' fill='none' xmlns='http://www.w3.org/2000/svg'>
<path d='M8 16L14 10L8 4' stroke='currentColor' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'/>
</svg>
</button>
</div>
</nav>
<!-- Pagination Navigation Script -->
<script>
(function() {
const pageUrls = ["performance.html","performance_p2.html","performance_p3.html"];
const pageInput = document.getElementById('page-input');
if (pageInput) {
pageInput.addEventListener('change', function() {
const pageNum = parseInt(this.value);
if (pageNum >= 1 && pageNum <= pageUrls.length) {
window.location.href = pageUrls[pageNum - 1];
} else {
this.value = this.getAttribute('value');
}
});
pageInput.addEventListener('keypress', function(e) {
if (e.key === 'Enter') {
this.blur();
}
});
}
})();
</script>
```
```{=html}
<style>
/* Ultra-Minimal Pagination Navigation */
.pagination-nav {
position: sticky;
bottom: 2rem;
margin: 3rem auto 2rem auto;
max-width: 240px;
z-index: 100;
}
.pagination-container {
display: flex;
align-items: center;
justify-content: center;
gap: 1rem;
padding: 0.5rem 1rem;
background: transparent;
}
.pagination-btn {
display: flex;
align-items: center;
justify-content: center;
width: 32px;
height: 32px;
padding: 0;
background: transparent;
color: var(--bs-secondary, #6c757d);
text-decoration: none;
border-radius: 0.375rem;
transition: all 0.15s ease;
border: none;
cursor: pointer;
}
.pagination-btn:hover:not(.pagination-disabled) {
background: var(--bs-light, #f8f9fa);
color: var(--bs-body-color, #212529);
}
.pagination-btn:active:not(.pagination-disabled) {
background: var(--bs-gray-200, #e9ecef);
}
.pagination-btn.pagination-disabled {
color: var(--bs-gray-300, #dee2e6);
cursor: default;
}
.pagination-icon {
flex-shrink: 0;
opacity: 0.7;
}
.pagination-btn:hover:not(.pagination-disabled) .pagination-icon {
opacity: 1;
}
.pagination-info {
display: flex;
align-items: center;
gap: 0.5rem;
font-size: 0.875rem;
color: var(--bs-secondary, #6c757d);
}
.pagination-input {
width: 2.5rem;
padding: 0.25rem 0.5rem;
text-align: center;
border: 1px solid transparent;
border-radius: 0.25rem;
font-size: 0.875rem;
font-weight: 500;
color: var(--bs-body-color, #212529);
background: transparent;
transition: all 0.15s ease;
}
.pagination-input:hover {
background: var(--bs-light, #f8f9fa);
border-color: var(--bs-border-color, #dee2e6);
}
.pagination-input:focus {
outline: none;
background: var(--bs-body-bg, #fff);
border-color: var(--bs-primary, #0d6efd);
box-shadow: 0 0 0 2px rgba(13, 110, 253, 0.1);
}
.pagination-input::-webkit-inner-spin-button,
.pagination-input::-webkit-outer-spin-button {
opacity: 0;
}
.pagination-separator {
color: var(--bs-secondary, #6c757d);
font-weight: 400;
font-size: 0.875rem;
}
/* Responsive */
@media (max-width: 768px) {
.pagination-nav {
max-width: 220px;
bottom: 1.5rem;
}
.pagination-container {
gap: 0.75rem;
padding: 0.4rem 0.75rem;
}
.pagination-btn {
width: 30px;
height: 30px;
}
.pagination-icon {
width: 16px;
height: 16px;
}
.pagination-info {
font-size: 0.8rem;
}
.pagination-input {
width: 2.25rem;
font-size: 0.8rem;
}
}
@media (max-width: 480px) {
.pagination-nav {
max-width: 200px;
}
.pagination-btn {
width: 28px;
height: 28px;
}
}
/* Dark mode support */
@media (prefers-color-scheme: dark) {
.pagination-btn:hover:not(.pagination-disabled) {
background: rgba(255, 255, 255, 0.05);
}
.pagination-btn:active:not(.pagination-disabled) {
background: rgba(255, 255, 255, 0.1);
}
.pagination-input:hover {
background: rgba(255, 255, 255, 0.05);
}
}
/* Override Quarto's back-to-top button position */
.back-to-top {
right: 2rem !important;
left: auto !important;
}
@media (max-width: 768px) {
.back-to-top {
right: 1rem !important;
}
}
</style>
```